Research Scientist Intern, AI Molecular Design (Winter/Spring)
Palo Alto, CA, United States3w ago
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Key details
- Salary
- $96K–$120K/yr
- Location
- Palo Alto, CA, United States
- Work style
- Onsite
- Level
- Intern
- Job type
- Internship
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 3w ago
Skills
About the role
Qualifications:
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M.S. or Ph.D. student (or evidence of equivalent level of expertise) in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Available for a 3-month, in-person internship in Palo Alto, CA, and authorized to work in the U.S. for the duration of the internship. Internship is expected to start January 2027.
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Skilled in developing, implementing, and debugging deep learning methods/models in popular frameworks, such as JAX, TensorFlow, or PyTorch, with an interest in generative models, graph neural networks, or large-scale deep learning applications.
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Strong theoretical foundation (e.g., statistics, optimization, graph theory, linear algebra).
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Passion for interdisciplinary research (emphasizing the intersection of AI and Biology), and willingness to acquire necessary domain knowledge.
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Motivated and self-driven with the ability to operate with partial descriptions of high-level objectives (as is typical in a start-up environment).
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Familiarity with software engineering best practices (version control, documentation, etc).
Nice to Have:
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3 year PhD student and above.
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Proven track record in research and innovation demonstrated through contributions in top-tier AI/ML (e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICLR) and/or core biology (e.g., Nature, Science, or Cell) journals and conferences.
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Intern experience in industry (e.g., OpenAI, FAIR, Deepmind, Google Research).
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Hands-on experience working at the intersection of AI and Biology, particularly protein structure prediction, protein sequence/structure modeling, or molecular design.
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Experience with biological structure prediction algorithms or models such as AlphaFold2/3 or RoseTTAFold.
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Experience in generative modeling for biological structures and sequences, including diffusion models, flow matching, or related approaches.
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Experience in large-scale distributed training and inference.
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Open-source contributions, especially if used by others.